Wenhui Wang1, Sen Yang2, Xiang Zhang2, Jing Li2. 1. Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA. 2. Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.
Abstract
MOTIVATION: The emergence of network medicine not only offers more opportunities for better and more complete understanding of the molecular complexities of diseases, but also serves as a promising tool for identifying new drug targets and establishing new relationships among diseases that enable drug repositioning. Computational approaches for drug repositioning by integrating information from multiple sources and multiple levels have the potential to provide great insights to the complex relationships among drugs, targets, disease genes and diseases at a system level. RESULTS: In this article, we have proposed a computational framework based on a heterogeneous network model and applied the approach on drug repositioning by using existing omics data about diseases, drugs and drug targets. The novelty of the framework lies in the fact that the strength between a disease-drug pair is calculated through an iterative algorithm on the heterogeneous graph that also incorporates drug-target information. Comprehensive experimental results show that the proposed approach significantly outperforms several recent approaches. Case studies further illustrate its practical usefulness. AVAILABILITY AND IMPLEMENTATION: http://cbc.case.edu CONTACT: jingli@cwru.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The emergence of network medicine not only offers more opportunities for better and more complete understanding of the molecular complexities of diseases, but also serves as a promising tool for identifying new drug targets and establishing new relationships among diseases that enable drug repositioning. Computational approaches for drug repositioning by integrating information from multiple sources and multiple levels have the potential to provide great insights to the complex relationships among drugs, targets, disease genes and diseases at a system level. RESULTS: In this article, we have proposed a computational framework based on a heterogeneous network model and applied the approach on drug repositioning by using existing omics data about diseases, drugs and drug targets. The novelty of the framework lies in the fact that the strength between a disease-drug pair is calculated through an iterative algorithm on the heterogeneous graph that also incorporates drug-target information. Comprehensive experimental results show that the proposed approach significantly outperforms several recent approaches. Case studies further illustrate its practical usefulness. AVAILABILITY AND IMPLEMENTATION: http://cbc.case.edu CONTACT: jingli@cwru.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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